Background: Chronic respiratory diseases are an important global issue, particularly in Asia, where burden patterns vary widely across countries. With more than half the world's population living in Asia, understanding the national and regional burden of chronic respiratory diseases is essential; however, research on this area remains inadequate. We aimed to investigate the burden of chronic respiratory diseases in Asia at national and regional levels, and to identify key risk factors.

Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study 2023 provides estimates for assessing the burden of chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD), and pulmonary sarcoidosis. We focused on 34 countries in Asia, encompassing the high-income Asia Pacific region and central, east, south, and southeast Asia. Estimates for age-standardised prevalence and disability-adjusted life-year (DALY) rates per 100 000 population, including 95% uncertainty intervals (UIs), were extracted by location, sex, year, and Socio-demographic Index (SDI). The average annual percentage change was calculated and presented as a percentage with 95% CIs. Estimates of modifiable attributable risk factors for DALYs and mortality were also included.

Findings: In Asia, the age-standardised prevalence and DALY rates for chronic respiratory diseases generally declined from 1990 to 2023; however, the trend varied substantially by disease and country. In 2023, the age-standardised prevalence rate of COPD was highest in south Asia (3044·18 [95% UI 2748·67-3303·04] per 100 000 population), while the age-standardised asthma prevalence rate was highest in the high-income Asia Pacific region (4870·24 [4046·70-5962·78] per 100 000 population) and southeast Asia (4778·18 [3970·25-5735·61] per 100 000 population). Despite southeast Asia and the high-income Asia Pacific region having a similar age-standardised asthma prevalence rate, southeast Asia had a higher age-standardised DALY rate (508·67 [95% UI 394·89-669·92] per 100 000 population) compared with the high-income Asia Pacific region (204·40 [129·23-290·41] per 100 000 population). A decrease in the age-standardised DALY rate for chronic respiratory diseases was observed with increasing SDI, contrasting with its prevalence patterns. Age-standardised DALY rates of COPD decreased in all Asian countries except for Georgia (average annual percentage change 1·37 [95% CI 1·26-1·48]) and Kazakhstan (0·73 [0·55-0·93]), and age-standardised DALY rates of asthma decreased in all countries. Smoking and ambient particulate matter pollution were identified as leading attributable risk factors for chronic respiratory diseases across Asia. Household air pollution from solid fuels was a regionally pronounced risk factor for chronic respiratory diseases, particularly in south Asia (age-standardised DALY rate 657·58 [95% UI 485·04-880·45] per 100 000 population). Although smoking was a major risk factor in males, ambient particulate matter pollution and secondhand smoke emerged as important attributable risk factors for chronic respiratory diseases in females.

Interpretation: Countries with lower SDI had markedly higher DALY rates, highlighting the need to address socioeconomic and health-care inequities. Household air pollution from solid fuels continues to impose a substantial but preventable burden in south Asia, calling for clean energy adoption and improved ventilation.

Funding: Gates Foundation.

Background: Hypertension is a major cause of death and disability, and undiagnosed cases are particularly dangerous as they can cause severe damage without timely treatment. The aim of the study was to identify risk factors for hypertension and undiagnosed hypertension in rural areas of Bangladesh using advanced Machine Learning (ML) algorithms.

Methods: This study involved 1,603 respondents, selected through a cross-sectional survey using a multistage cluster random sampling technique. Four ML algorithms, including Gradient Booster (GB), Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), were used in this study. Risk factors for hypertension and undiagnosed hypertension were identified using the best-performing ML model, selected based on metrics such as accuracy, sensitivity, specificity, precision, F1 score, receiver operating characteristics-area under the curve (ROC-AUC), and calibration plot.

Results: The prevalence of hypertension was 15.5%, slightly higher than the 15.4% for undiagnosed hypertension. In predicting the risk of both hypertension and undiagnosed hypertension, the LR model outperformed other ML models across most evaluation metrics. For hypertension, it achieved higher performance in terms of precision (0.580), F1 score (0.550), ROC-AUC (0.729; 95% CI: 0.677-0.779), and calibration. Similarly, for undiagnosed hypertension, the LR model showed better precision (0.580), ROC-AUC (0.596; 95% CI: 0.537-0.654), and calibration compared to other models. The risk factors for hypertension and undiagnosed hypertension differed notably. Key risk factors for undiagnosed hypertension included being overweight or obese, the absence of chronic diseases or cardiovascular disease (CVD), being male, non-use of tobacco, older age (above 50 years), being currently married, non-smoking status, having diabetes, and having no formal education.

Conclusion: The findings emphasize the urgent need for enhanced national and regional public health initiatives to improve the detection and awareness of hypertension in rural Bangladesh. Further research is important to validate the findings.

Background: Hypertension poses a significant public health challenge in Bangladesh, particularly among rural populations with limited access to healthcare. Thus, this study aimed to identify the influential factors associated with hypertension and prehypertension in rural Bangladesh using sophisticated machine learning (ML) methods.

Methods: A total of 1603 respondents were selected in this study using a multistage random sampling from a cross-sectional survey. Five commonly used sophisticated ML algorithms were used. The predictive performance of these models was evaluated using standard validation metrics. Influential variables were identified and ranked using SHapley Additive exPlanations (SHAP) technique.

Results: The prevalence of hypertension and prehypertension was 30.9% and 40.8%, respectively. The XGB model outperformed other ML models in predicting hypertension (accuracy: 74.3%, ROC: 75.8%), while the LR model was better at predicting prehypertension (accuracy: 59.2%, ROC: 54.1%). Top factors in predicting hypertension were older age, being overweight or obese, having a past or no smoking history, reporting no chronic disease, and having a family history of hypertension, whereas top factors for pre-hypertension were current smoking status, absence of cardiovascular disease, being in a younger or middle-aged group, having no family history of hypertension, and current employment.

Conclusion: Three out of ten people in rural areas were hypertensive, while two out of five were prehypertensive. The ML models had the potential to predict hypertension. The current findings highlight an urgent need for strengthened national and regional public health initiatives to improve hypertension detection, awareness, and management in rural Bangladesh.

Life expectancy is a key indicator of population health and an important guide for health policy1,2. Although Asia represents approximately 60% of the global population, studies of longitudinal trends in life expectancy and their underlying drivers across Asian countries remain limited, with most previous research focused on western or high-income settings3, 4, 5–6. Here we provide a comprehensive analysis of life expectancy, cause-specific mortality and risk factors in 1990–2023 across 34 Asian countries and territories, utilizing data from the Global Burden of Disease Study 20231,7. Life expectancy increased in all countries and territories between 1990 and 2023, with the largest annual gains observed in South Asia and the smallest annual gains in high-income Asia Pacific countries and territories. Reductions in cardiovascular disease mortality were the primary contributors to life expectancy gains in Central Asia, East Asia and high-income Asia Pacific, whereas declines in diarrhoeal diseases and tuberculosis contributed most in South and Southeast Asia. In 2019–2023, life expectancy declined in several Asian regions, largely driven by the COVID-19 pandemic, with a nearly two-year loss in the first year of the pandemic. The causes of changes in life expectancy and the contributing risk factors varied across regions and countries/territories. Therefore, under the principles of proportional universalism, proactive and effective policies at both regional and national levels are essential to reduce premature mortality and reduce life expectancy inequalities across Asia. 

Background Child growth failure (CGF), which includes underweight, wasting, and stunting, is among the factorsmost strongly associated with mortality and morbidity in children younger than 5 years worldwide. Poor height andbodyweight gain arise from a variety of biological and sociodemographic factors and are associated with increasedvulnerability to infectious diseases. We used data from the Global Burden of Diseases, Injuries, and Risk FactorsStudy (GBD) 2023 to estimate CGF prevalence, the risk of infectious diseases associated with CGF, and the diseasemortality, morbidity, and overall burden associated with CGF.

Methods In this analysis we estimated the all-cause and cause-specific (diarrhoea, lower respiratory tract infections,malaria, and measles) disability-adjusted life-years (DALYs) lost and mortality associated with stunting, wasting,underweight, and CGF in aggregate. We combined the burden associated with mild, moderate, and severe forms ofCGF: stunting was defined as height-for-age Z scores (HAZ) less than –1, underweight was defined as weight-for-ageZ scores (WAZ) less than –1, and wasting was defined as weight-for-height Z scores (WHZ) less than –1, according toWHO Child Growth Standards. Population-level continuous distributions of HAZ, WAZ, and WHZ were estimatedfor 2000 to 2023 using data from surveys, literature, and individual-level study data. The risk of incidence of, andmortality due to, diarrhoea, lower respiratory infections, malaria, and measles was separately estimated in a metaregressionframework from longitudinal cohort data for Z scores less than –1. Finally, fatal outcomes associated withthese diseases were estimated with vital registration, verbal autopsy, and case-fatality data, while non-fatal outcomeswere estimated with surveys as well as health-care utilisation and case reporting data. The exposure prevalence andrelative risk estimates were from continuous distributions, allowing for direct assessment of the attributable fractionsfor mild, moderate, and severe stunting, underweight, wasting, and the combined impact of child growth failurewithin populations. All estimates were age-specific, sex-specific, geography-specific, and year-specific.

Findings We estimated that, in children younger than 5 years in 2023, CGF was associated with 79·4 million(95% uncertainty interval [UI] 47·0–106) DALYs lost and 880 000 (517 000–1 170 000) deaths. This represented17·9% (10·6–23·8) of 444 million (434–457) total under-5 DALYs and 18·8% (11·1–25·0) of all 4·67 million(4·59–4·75) under-5 deaths. Compared to stunting (33·0 million [24·1–42·2] DALYs, 373 000 [272 000–477 000]deaths) and wasting (39·2 million [23·8–53·0] DALYs, 428 000 [256 000–583 000] deaths), childhood underweight wasassociated with the largest share of CGF-related disease burden: 52·2 million (21·9–75·1) DALYs and 573 000(236 000–824 000) deaths in children younger than 5 years in 2023.

Interpretation CGF remains a leading factor associated with death and disability in children younger than 5 years,despite global attention and focused interventions to reduce the prevalence of associated CGF indicators. Our findingsunderscore the need for policies, strategies, and interventions that focus on all indicators of CGF to reduce itsassociated health burden.

Background: A spinal cord injury (SCI) is a complex medical condition that often results in functional impairments and severe morbidity. This study aims to assess the reliability and validity of the Bengali version of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) (36-item version) in individuals with SCI.

Methods: A total of 144 participants (81% men) underwent interviews using WHODAS 2.0, Spinal Cord Independence Measure (SCIM) and the SF-36 Health Survey 2.0 (SF-36), alongside collecting socio-demographic and injury-related data. (e.g., age, sex, injury type, and injury severity). A sub-sample of 62 participants underwent a repeat assessment 8-10 days later. Reliability was measured through internal consistency (Cronbach's alpha) and test-retest reliability (Intra-class correlation coefficients, ICC). Validity was assessed via internal validity (e.g., scaling assumptions, floor and ceiling effects), convergent and discriminative validity, along with Rasch analyses for dimensionality and item fit.

Results: Internal consistency ranged from questionable (alpha=0.69 for getting around) to excellent (alpha=0.92 for life activities). Test-retest reliability varied from moderate (ICC=0.58 for cognition) to excellent (ICC=0.92 for self-care). Correlations with SCIM and SF-36 2.0 supported convergent validity. Discriminative validity was confirmed by significant differences between individuals with paraplegia and tetraplegia across all WHODAS-2 domains. Notably, all items in the Cognition domain did not fit the Rasch model. 

Conclusion: The Bengali version of WHODAS 2.0 demonstrates acceptable reliability and validity in individuals with SCI in a resource-limited setting. While overall performance is good, limitations were observed in the Cognition domain's item fit. This study advocates for the clinical use of WHODAS 2.0 but emphasizes the importance of further validation in larger samples.

Background: The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023.

Methods: Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population.

Findings: We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI.

Interpretation: A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice.

Funding: Gates Foundation, Queensland Health, and University of Queensland.

Machine learning (ML) excels over regression by automatically capturing complex, nonlinear relationships and interactions, enabling more flexible and accurate predictions without strict assumptions. This study focuses on developing ML-based predictive models for key post-PCI outcomes: 30-day mortality, in-hospital major bleeding, and one-year mortality. Data from 104,665 consecutive PCI cases in the Victorian Cardiac Outcomes Registry (VCOR), collected between 2013 and 2022, will be analyzed. Candidate variables, informed by prior systematic reviews and dataset availability, will undergo multiple imputations for missing values. The Boruta method will be applied to identify influential predictors. Risk-adjusted models will be developed using sophisticated ML algorithms,with performance compared across standard metrics for validation. The dataset will be split, optimized via 10-fold cross-validation, and class imbalance addressed using Adaptive Synthetic resampling technique. SHapley Additive exPlanations will interpret the most influential predictors. The variables from the best model will be converted into simplified numeric scores. External validation will be performed using the Tasmanian dataset or equivalent datasets. This study is expected to identify the most influential variables associated with 30-day all-cause mortality, in-hospital major bleeding, and long-term mortality post-PCI. These variables will form the basis for developing robust risk-scoring models to support clinical decision-making and outcome prediction.

Background: Bangladesh continues to grapple with a persistently high infant mortality rate, currently at 38 deaths per 1,000 live births. Adolescent pregnancy poses significant health risks for both mothers and infants globally, yet its specific impact on infant mortality in Bangladesh remains underexplored. Therefore, this study aims to investigate the association between adolescent pregnancy and infant mortality in Bangladesh.Methods: This cross-sectional study analyzed data from the Bangladesh Demographic and Health Survey 2017-18, focusing on 8,759 infants born to women aged 15-49 years. Adolescent pregnancies were categorized into four groups: <16 years, 16-17 years, 18-19 years, and >19 years. Potential covariates included sociodemographic factors (mothers’ age, fathers’ occupation, religion), contextual factors (place of delivery, access to media and technology), and healthcare utilization (antenatal and postnatal care). Bivariate logistic regression assessed associations between adolescent pregnancy and infant mortality, presenting adjusted odds ratios (AOR) with 95% confidence intervals (CI) while controlling for these covariates.Results: The mean age of mothers at first birth was 18.53 years. Among the 8,759 infants studied, 328 (3.74%) died before reaching 12 months of age. Infants born to mothers younger than 16 years initially showed higher odds of mortality (AOR: 1.45, 95% CI: 1.05-2.01); this association persisted even after adjusting for sociodemographic and contextual factors (AOR: 1.41, 95% CI: 1.01-1.96). However, after controlling for healthcare utilization, the relationship was no longer statistically significant (AOR: 1.06, 95% CI: 0.56-1.99).Conclusions: Delaying childbirth from adolescence to adulthood may reduce infant mortality in Bangladesh. However, adolescent pregnancy alone does not increase infant mortality risk after accounting for healthcare utilization, such as antenatal and postnatal care. Improving access to quality healthcare is crucial for lowering infant mortality. Future cohort studies are needed to better understand the relationship between maternal age and infant health outcomes.

Background

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations.

Methods

GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds.

Findings

The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6–47·0) in 1990 to 63·4 years (63·1–63·7) in 2023. For males, mean age increased from 45·4 years (45·1–45·7) to 61·2 years (60·7–61·6), and for females it increased from 48·5 years (48·1–48·8) to 65·9 years (65·5–66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9–81·0) and for males 74·8 years (74·8–74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5–38·4) for females and 35·6 years (35·2–35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value.

Interpretation

We examined global mortality patterns over the past three decades, highlighting—with enhanced estimation methods—the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales.

Funding

Gates Foundation.